An Interval Type-2 Fuzzy-based System to Create Building Information Management Models from 2D Floor Plan Images

• Published in: 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) pp. 1 - 7
• Main Authors: Leon-Garza, Hugo, Hagras, Hani, Pena-Rios, Anasol, Conway, Anthony, Owusu, Gilbert
• Format: Conference Proceeding
• Language: English
• Published: IEEE 11.07.2021
• Subjects: BIM; Building information management; Deep learning; floor plans; Image segmentation; interval type-2 fuzzy logic; Measurement; patch-based segmentation; Pipelines; rule-based systems; semantic segmentation; Semantics; Transforms
• ISSN: 1558-4739
• DOI: 10.1109/FUZZ45933.2021.9494464

Building Information Modelling (BIM) is a process that contain all the necessary information to manage the construction project across all its lifecycle. This benefits not only the construction industry but other industries such as utility companies that need to perform tasks inside of buildings and will need to access information about its elements. However, one of the biggest challenges is the digitalisation of the existing infrastructure. The use of semantic segmentation techniques could enable the transformation of infrastructure legacy data, such as 2D floor plans images, to open-standard BIM models. In this paper, we propose a processing pipeline to transform 2D floor plan images into BIM models. The pipeline makes use of an interval Type-2 Fuzzy Rule-based System (FRBS) that has an Intersection over Union metric value of 98.62% outperforming the Type-1 version of the model. Moreover, the proposed model is highly transparent, and it allows end-users to augment it using expert knowledge, something that is not possible in deep learning opaque-box models.